WMO 72417 and surroundings

An article on WUWT about a USA temperature timeseries for a site called Dale Enterprises caused me to wonder and discover what data I already had which might add interest and perhaps insight. This turned out to indeed be interesting.

The problem is how to show a sensible result so I will describe what data and what I did with it. Hopefully then an otherwise confusing plot will make sense.

If you are looking for scandal, move along, nothing here.

I have several SQL databases containing temperature timeseries, part of an ongoing work where I have a lot more data to add.

The World Meterological Office station number was deduced, Dale Enterprises is a substation. This works as follows, there is a primary site and that has a suffix of zero. Any nearby sites are given a non-zero suffix.

Since I have a number of gridded datasets in databases as part of the Query software, it is trivial to extract a timeseries for a gridcell over a time period.

Hadcrut3 currect version (to Jan 2010)

GHCN RVose which is a kept a reasonably up to date dataset, to Sept 2009

Those are 5 degree gridded. Unfortunately this station is not in the centre of a grid cell so I allowed the software to choose a sensible actual cell. Asked for 39N 79W, got 37.5N 77.5W. Does that matter? The results say no.

Pondering over the data I concluded the best idea was take a representative period where all data is present and do a combined plot. The reason will become obvious later. Period 1950 through end 1969, post WW2 technology improvements and pre-computerisation.

None of the processing is automated so the following took quite a few hours. The result was put together and part process using OpenOffice 3.2. This is a new release where I was fearful of yet more showstopper bugs but I have been pleasantly surprised that it may well turn out to be the most stable to date, better than 3.0.1 which was the last fairly stable version. Particularly a number of items are massively faster, largely because they were snail’s pace before, been fixed. It seems too that they have also managed to fix the dire memory corruption problems in the graphing module (crazy things happened and crashes were frequent)

I removed the annual cycle from the station data using my own software. This optimises to best match with the data for all apt harmonics of one year. This uses Fourier, not common math. The result is at zero offset to itself, the offset can be added back but is not needed here.

Missing data is handled gracefully, with no material effect. The plot has gaps.

The final step was take the common average of all the datasets over the 1950-1970 period, compute the offset from the reference, Hadcrut3 and adjust each to a common mean.

Here is the result, click for full size.

I did not expect this result, which rather demonstrates a pleasing commonality. If you looked at the data it is all over the place.

Observation: The GHCN station data often has single months missing in otherwise complete data. Experience has led me to suspect this is hinting these are often dataset joins.

GHCN72417-004

GLENVILLE

1ENE

38.93

-80.82

219

GHCN72417-007

OAKLAND

1SE

39.4

-79.4

737

GHCN72417-006

PARSONS

1NE

39.1

-79.67

539

GHCN72417-008

MANNINGTON

7WNW

39.53

-80.5

335

GHCN72417-009

CUMBERLAND

2

39.63

-78.75

222

GHCN72417-001

DALE

ENTERPRISE

38.45

-78.93

426

GHCN72417-003

ELKINS/RANDOLPH

CO

38.88

-79.85

GHCN72417-010

MORGANTOWN/FAA

AIRPORT

39.65

-79.92

378

GHCN72417-005

BUCKHANNON

UNITED

-80.22

443

1891

GHCN72417-002

PICKENS

4SSE

38.62

-80.18

1066

CRU72417-000

ELKINS/RANDOLPH-CO-A

USA

38.88

-79.85

604

All the datasets are well known so I thank the authors but do not specifically cite.

As usual, if you want the data etc. contact me.

Coda : –

Wondering whether I had done the plot right I decided to restrict the time analysis. Doesn’t materially change anything but here it is anyway.